QC
A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.
Report generated on 2024-05-09, 10:07 EDT based on data in:
/vf/users/DCEG_Trios/novaseq_benchmark/TriosCompass_v2/output/collectmultiplemetrics/vf/users/DCEG_Trios/novaseq_benchmark/TriosCompass_v2/output/collectwgsmetrics/vf/users/DCEG_Trios/novaseq_benchmark/TriosCompass_v2/output/fastp/vf/users/DCEG_Trios/novaseq_benchmark/TriosCompass_v2/output/fastqc/vf/users/DCEG_Trios/novaseq_benchmark/TriosCompass_v2/output/fastq_screen/vf/users/DCEG_Trios/novaseq_benchmark/TriosCompass_v2/output/gatk_markdup/vf/users/DCEG_Trios/novaseq_benchmark/TriosCompass_v2/output/dragen_gvcf
General Statistics
| Sample Name | M Input reads | Unmap | Dup | Prop pair | Med IS | Variants | Sex | % GC | Insert Size | Duplication | M Reads | Median Coverage | Bases ≥ 30X | % Dups | % GC | M Seqs |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SC501095 | 2555.0 M | 3.8% | 4.5% | 94.0% | 332 | 4849223 | XY | 41% | 346 bp | 4.3% | 89.0X | 93% | ||||
| SC501095_B22CHLTLT3.R1 | 17.6% | 40% | 1118.0 M | |||||||||||||
| SC501095_B22CHLTLT3.R2 | 18.7% | 40% | 1118.0 M | |||||||||||||
| SC501096 | 2380.0 M | 3.4% | 4.4% | 94.3% | 369 | 4904207 | XX | 41% | 384 bp | 4.0% | 86.0X | 93% | ||||
| SC501096_B22CHLTLT3.R1 | 16.7% | 40% | 1026.9 M | |||||||||||||
| SC501096_B22CHLTLT3.R2 | 16.4% | 40% | 1026.9 M | |||||||||||||
| SC501105 | 2617.6 M | 3.4% | 7.3% | 94.7% | 302 | 4885301 | XX | 41% | 324 bp | 7.0% | 86.0X | 93% | ||||
| SC501105_B22CHLTLT3.R1 | 18.1% | 40% | 1150.9 M | |||||||||||||
| SC501105_B22CHLTLT3.R2 | 19.4% | 40% | 1150.9 M | |||||||||||||
| SC501108 | 2465.7 M | 3.5% | 6.8% | 94.1% | 380 | 4867227 | XY | 41% | 376 bp | 6.2% | 83.0X | 93% | ||||
| SC501108_B22CHLTLT3.R1 | 18.5% | 40% | 1044.0 M | |||||||||||||
| SC501108_B22CHLTLT3.R2 | 18.8% | 40% | 1044.0 M | |||||||||||||
| SC501110 | 2223.9 M | 3.2% | 8.3% | 94.4% | 347 | 4884094 | XX | 41% | 362 bp | 7.8% | 76.0X | 92% | ||||
| SC501110_B22CHLTLT3.R1 | 16.7% | 40% | 974.8 M | |||||||||||||
| SC501110_B22CHLTLT3.R2 | 16.6% | 40% | 974.8 M | |||||||||||||
| SC501111 | 2514.8 M | 3.5% | 11.9% | 93.9% | 385 | 4844316 | XY | 41% | 386 bp | 11.2% | 85.0X | 93% | ||||
| SC501111_B22CHLTLT3.R1 | 20.6% | 40% | 1096.7 M | |||||||||||||
| SC501111_B22CHLTLT3.R2 | 20.5% | 40% | 1096.7 M | |||||||||||||
| SD162355 | 2694.6 M | 3.7% | 10.4% | 93.8% | 326 | 4896047 | XY | 41% | 327 bp | 10.1% | 87.0X | 93% | ||||
| SD162355_A22FKHJLT3.R1 | 21.0% | 41% | 1196.4 M | |||||||||||||
| SD162355_A22FKHJLT3.R2 | 22.4% | 41% | 1196.4 M | |||||||||||||
| SD162357 | 2583.1 M | 3.4% | 18.1% | 94.2% | 390 | 4877362 | XY | 42% | 380 bp | 17.8% | 82.0X | 93% | ||||
| SD162357_A22FKHJLT3.R1 | 23.3% | 41% | 1157.1 M | |||||||||||||
| SD162357_A22FKHJLT3.R2 | 24.5% | 41% | 1157.1 M | |||||||||||||
| SD162359 | 2751.1 M | 3.2% | 9.6% | 94.8% | 296 | 4946727 | XX | 42% | 320 bp | 9.5% | 90.0X | 93% | ||||
| SD162359_A22FKHJLT3.R1 | 19.6% | 41% | 1240.9 M | |||||||||||||
| SD162359_A22FKHJLT3.R2 | 21.0% | 41% | 1240.9 M | |||||||||||||
| quality_yield | 2481.7 M |
DRAGEN
DRAGEN is a Bio-IT Platform that provides ultra-rapid secondary analysis of sequencing data using field-programmable gate array technology (FPGA).
Mapping metrics
Mapping metrics, similar to the metrics computed by the samtools-stats command. Shown on per read group level. To see per-sample level metrics, refer to the general stats table.
| Sample Name | M Input reads | Paired | QC-fail | Unmap | Dup | Prop pair | Discord | Singleton | Diff chr, MQ⩾10 | Med IS | M Alignments | Sec'ry |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SC501095 | 2555.0 M | 100.0% | 0.00% | 3.8% | 4.5% | 94.0% | 1.22% | 0.94% | 0.85% | 332 | 2467.2 M | 0.00% |
| SC501096 | 2380.0 M | 100.0% | 0.00% | 3.4% | 4.4% | 94.3% | 1.30% | 1.07% | 0.92% | 369 | 2307.7 M | 0.00% |
| SC501105 | 2617.6 M | 100.0% | 0.00% | 3.4% | 7.3% | 94.7% | 1.05% | 0.87% | 0.74% | 302 | 2537.8 M | 0.00% |
| SC501108 | 2465.7 M | 100.0% | 0.00% | 3.5% | 6.8% | 94.1% | 1.32% | 1.10% | 0.82% | 380 | 2386.9 M | 0.00% |
| SC501110 | 2223.9 M | 100.0% | 0.00% | 3.2% | 8.3% | 94.4% | 1.40% | 0.96% | 0.95% | 347 | 2159.4 M | 0.00% |
| SC501111 | 2514.8 M | 100.0% | 0.00% | 3.5% | 11.9% | 93.9% | 1.50% | 1.09% | 1.06% | 385 | 2433.9 M | 0.00% |
| SD162355 | 2694.6 M | 100.0% | 0.00% | 3.7% | 10.4% | 93.8% | 1.57% | 0.95% | 1.21% | 326 | 2606.5 M | 0.00% |
| SD162357 | 2583.1 M | 100.0% | 0.00% | 3.4% | 18.1% | 94.2% | 1.43% | 0.91% | 1.08% | 390 | 2503.2 M | 0.00% |
| SD162359 | 2751.1 M | 100.0% | 0.00% | 3.2% | 9.6% | 94.8% | 1.11% | 0.80% | 0.83% | 296 | 2670.8 M | 0.00% |
Mapped / paired / duplicated
Distribution of reads based on pairing, duplication and mapping.
Variant calling
Variant calling metrics. Metrics are reported for each sample in multi sample VCF and gVCF files. Based on the run case, metrics are reported either as standard VARIANT CALLER or JOINT CALLER. All metrics are reported for post-filter VCFs, except for the "Filtered" metrics which represent how many variants were filtered out from pre-filter VCF to generate the post-filter VCF.
| Sample Name | Variants | Multiallelic | SNP | Ins | Del | Ti/Tv | Het/Hom | Callability | M VC reads |
|---|---|---|---|---|---|---|---|---|---|
| SC501095 | 4849223 | 1.9% | 80.4% | 9.3% | 9.3% | 2.0 | 1.5 | NA | 2316.1 M |
| SC501096 | 4904207 | 1.9% | 80.4% | 9.5% | 9.5% | 2.0 | 1.6 | NA | 2170.4 M |
| SC501105 | 4885301 | 1.9% | 80.4% | 9.5% | 9.5% | 2.0 | 1.6 | NA | 2311.6 M |
| SC501108 | 4867227 | 1.9% | 80.5% | 9.3% | 9.3% | 2.0 | 1.6 | NA | 2181.9 M |
| SC501110 | 4884094 | 1.9% | 80.4% | 9.5% | 9.5% | 2.0 | 1.6 | NA | 1947.3 M |
| SC501111 | 4844316 | 1.9% | 80.4% | 9.3% | 9.3% | 2.0 | 1.6 | NA | 2096.2 M |
| SD162355 | 4896047 | 1.9% | 80.4% | 9.3% | 9.3% | 2.0 | 1.6 | NA | 2281.8 M |
| SD162357 | 4877362 | 1.9% | 80.4% | 9.3% | 9.3% | 2.0 | 1.6 | NA | 1993.3 M |
| SD162359 | 4946727 | 2.0% | 80.5% | 9.5% | 9.5% | 2.0 | 1.6 | NA | 2356.1 M |
Target Bed Coverage Metrics
Coverage metrics over target Bed. All samples are based on the target_bed.
Press the Help button for details.
The following criteria are used when calculating coverage:
-
Duplicate reads and clipped bases are ignored.
-
DRAGEN V3.4 - 3.7: Only reads with
MAPQ>min MAPQand bases withBQ>min BQare considered -
DRAGEN V3.8 - 4.1: By default, reads with MAPQ < 1 and bases with BQ < 0 are ignored. You can use the qc-coverage-filters-n option to specify which BQ bases and MAPQ reads to filter out.
Considering only bases usable for variant calling, i.e. excluding:
-
Clipped bases
-
Bases in duplicate reads
-
Reads with
MAPQ<min MAPQ(default20) -
Bases with
BQ<min BQ(default10) -
Reads with
MAPQ=0(multimappers) -
Overlapping mates are double-counted
Each _coverage_metrics.csv file may have an associated _overall_mean_cov.csv file. The latter contains the 'Average alignment coverage over <source file>' metric. Information about <source file>s can be found in the section's description or in this drop-list below if the produced text is long. If input directory does not contain _overall_mean_cov files, then "No 'coverage bed/target bed/wgs' source file found" is printed.
| Sample Name | M Aln reads | Mb Aln bases | Depth | Uniformity(>0.2×mean) | Mean/med autosomal coverage |
|---|---|---|---|---|---|
| SC501095 | 2206.5 | 314731.2 | 107.13 x | 97.15 % | 0.97 x |
| SC501096 | 2083.9 | 303304.7 | 103.24 x | 96.63 % | 0.96 x |
| SC501105 | 2208.2 | 312665.2 | 106.41 x | 96.61 % | 0.97 x |
| SC501108 | 2082.2 | 300705.4 | 102.28 x | 97.17 % | 0.97 x |
| SC501110 | 1859.5 | 268686.0 | 91.51 x | 96.62 % | 0.97 x |
| SC501111 | 2001.8 | 290192.3 | 98.72 x | 97.20 % | 0.97 x |
| SD162355 | 2178.1 | 308900.6 | 104.90 x | 97.10 % | 0.97 x |
| SD162357 | 1905.5 | 276178.1 | 93.66 x | 97.18 % | 0.97 x |
| SD162359 | 2265.5 | 320819.8 | 108.75 x | 96.54 % | 0.97 x |
WGS Coverage Metrics
Coverage metrics over genome. All samples are based on the wgs.
Press the Help button for details.
The following criteria are used when calculating coverage:
-
Duplicate reads and clipped bases are ignored.
-
DRAGEN V3.4 - 3.7: Only reads with
MAPQ>min MAPQand bases withBQ>min BQare considered -
DRAGEN V3.8 - 4.1: By default, reads with MAPQ < 1 and bases with BQ < 0 are ignored. You can use the qc-coverage-filters-n option to specify which BQ bases and MAPQ reads to filter out.
Considering only bases usable for variant calling, i.e. excluding:
-
Clipped bases
-
Bases in duplicate reads
-
Reads with
MAPQ<min MAPQ(default20) -
Bases with
BQ<min BQ(default10) -
Reads with
MAPQ=0(multimappers) -
Overlapping mates are double-counted
Each _coverage_metrics.csv file may have an associated _overall_mean_cov.csv file. The latter contains the 'Average alignment coverage over <source file>' metric. Information about <source file>s can be found in the section's description or in this drop-list below if the produced text is long. If input directory does not contain _overall_mean_cov files, then "No 'coverage bed/target bed/wgs' source file found" is printed.
| Sample Name | M Aln reads | Mb Aln bases | Depth | Uniformity(>0.2×mean) | Mean/med autosomal coverage |
|---|---|---|---|---|---|
| SC501095 | 2206.5 | 314731.2 | 104.10 x | 94.23 % | 0.96 x |
| SC501096 | 2083.9 | 303304.7 | 100.34 x | 93.70 % | 0.96 x |
| SC501105 | 2208.2 | 312665.2 | 103.41 x | 93.69 % | 0.97 x |
| SC501108 | 2082.2 | 300705.4 | 99.47 x | 94.27 % | 0.97 x |
| SC501110 | 1859.5 | 268686.0 | 88.91 x | 93.77 % | 0.96 x |
| SC501111 | 2001.8 | 290192.3 | 95.98 x | 94.25 % | 0.96 x |
| SD162355 | 2178.1 | 308900.6 | 102.13 x | 94.10 % | 0.97 x |
| SD162357 | 1905.5 | 276178.1 | 91.37 x | 94.24 % | 0.97 x |
| SD162359 | 2265.5 | 320819.8 | 106.05 x | 93.55 % | 0.97 x |
Coverage distribution
Number of locations in the reference genome with a given depth of coverage.
For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position (Sims et al. 2014).
Bases of a reference sequence (y-axis) are groupped by their depth of coverage (0×, 1×, …, N×) (x-axis). This plot shows the frequency of coverage depths relative to the reference sequence for each read dataset, which provides an indirect measure of the level and variation of coverage depth in the corresponding sequenced sample.
If reads are randomly distributed across the reference sequence, this plot should resemble a Poisson distribution (Lander & Waterman 1988), with a peak indicating approximate depth of coverage, and more uniform coverage depth being reflected in a narrower spread. The optimal level of coverage depth depends on the aims of the experiment, though it should at minimum be sufficiently high to adequately address the biological question; greater uniformity of coverage is generally desirable, because it increases breadth of coverage for a given depth of coverage, allowing equivalent results to be achieved at a lower sequencing depth (Sampson et al. 2011; Sims et al. 2014). However, it is difficult to achieve uniform coverage depth in practice, due to biases introduced during sample preparation (van Dijk et al. 2014), sequencing (Ross et al. 2013) and read mapping (Sims et al. 2014).
This plot may include a small peak for regions of the reference sequence with zero depth of coverage. Such regions may be absent from the given sample (due to a deletion or structural rearrangement), present in the sample but not successfully sequenced (due to bias in sequencing or preparation), or sequenced but not successfully mapped to the reference (due to the choice of mapping algorithm, the presence of repeat sequences, or mismatches caused by variants or sequencing errors). Related factors cause most datasets to contain some unmapped reads (Sims et al. 2014).
Cumulative coverage hist
Number of locations in the reference genome with at least given depth of coverage.
For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position, while the breadth of coverage is the fraction of the reference sequence to which reads have been mapped with at least a given depth of coverage (Sims et al. 2014).
Defining coverage breadth in terms of coverage depth is useful, because sequencing experiments typically require a specific minimum depth of coverage over the region of interest (Sims et al. 2014), so the extent of the reference sequence that is amenable to analysis is constrained to lie within regions that have sufficient depth. With inadequate sequencing breadth, it can be difficult to distinguish the absence of a biological feature (such as a gene) from a lack of data (Green 2007).
For increasing coverage depths (1×, 2×, …, N×), coverage breadth is calculated as the percentage of the reference sequence that is covered by at least that number of reads, then plots coverage breadth (y-axis) against coverage depth (x-axis). This plot shows the relationship between sequencing depth and breadth for each read dataset, which can be used to gauge, for example, the likely effect of a minimum depth filter on the fraction of a genome available for analysis.
Coverage per contig
Average coverage per contig or chromosome. Calculated as the number of bases (excluding duplicate marked reads, reads with MAPQ=0, and clipped bases), divided by the length of the contig or (if a target bed is used) the total length of the target region spanning that contig.
Coverage per contig (non-main)
Non-main contigs: unlocalized (random), unplaced (chrU), alts (*_alt), mitochondria (chrM), EBV, HLA. Zoom in to see more contigs as all labels don't fit the screen.
Fragment length hist
Distribution of estimated fragment lengths of mapped reads per read group. Only points supported by at least 5 reads are shown to prevent long flat tail. The plot is also smoothed down to showing 300 points on the X axis to reduce noise.
Trimmer Metrics
Metrics on trimmed reads.
| Sample Name | Total input reads | Total input bases | Total input bases R1 | Total input bases R2 | Average input read length | Total trimmed reads | Total trimmed bases | Average bases trimmed per read | Average bases trimmed per trimmed read | Remaining poly-G K-mers R1 3prime | Remaining poly-G K-mers R2 3prime | Poly-G soft trimmed reads unfiltered R1 3prime | Poly-G soft trimmed reads unfiltered R2 3prime | Poly-G soft trimmed reads filtered R1 3prime | Poly-G soft trimmed reads filtered R2 3prime | Poly-G soft trimmed bases unfiltered R1 3prime | Poly-G soft trimmed bases unfiltered R2 3prime | Poly-G soft trimmed bases filtered R1 3prime | Poly-G soft trimmed bases filtered R2 3prime | Total filtered reads | Reads filtered for minimum read length R1 | Reads filtered for minimum read length R2 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SC501095 | 2554951260.0 | 365659479246.0 | 182671015190.0 | 182988464056.0 | 143.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.1 | 1.5 | 0.0 | 0.0 | 0.1 | 1.4 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| SC501096 | 2380036766.0 | 348283330343.0 | 174049534906.0 | 174233795437.0 | 146.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.1 | 0.1 | 1.6 | 0.0 | 0.0 | 0.0 | 1.5 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| SC501105 | 2617623186.0 | 372355127256.0 | 185951497273.0 | 186403629983.0 | 142.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.1 | 1.5 | 0.0 | 0.0 | 0.0 | 1.4 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| SC501108 | 2465729128.0 | 358404014957.0 | 179019948420.0 | 179384066537.0 | 145.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.1 | 0.1 | 1.6 | 0.0 | 0.0 | 0.0 | 1.5 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| SC501110 | 2223890036.0 | 323400398399.0 | 161614492216.0 | 161785906183.0 | 145.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9 | 0.1 | 1.4 | 0.0 | 0.0 | 0.0 | 1.3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| SC501111 | 2514803832.0 | 366659342143.0 | 182966652446.0 | 183692689697.0 | 145.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.4 | 0.1 | 1.9 | 0.0 | 0.0 | 0.0 | 1.8 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| SD162355 | 2694630052.0 | 384892728308.0 | 192024215197.0 | 192868513111.0 | 142.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.1 | 0.1 | 1.9 | 0.0 | 0.0 | 0.1 | 1.7 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| SD162357 | 2583116404.0 | 377045043113.0 | 188018447976.0 | 189026595137.0 | 145.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.4 | 0.1 | 1.9 | 0.0 | 0.0 | 0.1 | 1.8 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| SD162359 | 2751117024.0 | 392305678023.0 | 195972751622.0 | 196332926401.0 | 142.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8 | 0.1 | 1.4 | 0.0 | 0.0 | 0.1 | 1.3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Time Metrics
Time metrics for DRAGEN run. Total run time is less than the sum of individual steps because of parallelization.
DRAGEN-FastQC
DRAGEN-FastQC is a Bio-IT Platform that provides ultra-rapid secondary analysis of sequencing data using field-programmable gate array technology (FPGA).
Per-Position Mean Quality Scores
The mean quality value across each base position in the read.
To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).
Taken from the FastQC help:
The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.
Per-Sequence Quality Scores
The number of reads with average quality scores. Shows if a subset of reads has poor quality.
From the FastQC help: The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.
Sequence Length Distribution
The distribution of fragment sizes (read lengths) found. See the FastQC help
Per-Sequence GC Content
The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.
From the FastQC help: This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content. In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution. An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.
GC Content Mean Quality Scores
The mean quality value across each base position in the read.
To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).
Taken from the FastQC help:
The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.
Per-Position N Content
The percentage of base calls at each position for which an N was called.
From the FastQC help:
If a sequencer is unable to make a base call with sufficient confidence then it will
normally substitute an N rather than a conventional base call. This graph shows the
percentage of base calls at each position for which an N was called.
It's not unusual to see a very low proportion of Ns appearing in a sequence, especially
nearer the end of a sequence. However, if this proportion rises above a few percent
it suggests that the analysis pipeline was unable to interpret the data well enough to
make valid base calls.
Per-Position Sequence Content
The proportion of each base position for which each of the four normal DNA bases has been called.
To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor. To see the data as a line plot, as in the original FastQC graph, click on a sample track. From the FastQC help: Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called. In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other. It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.
Rollover for sample name
Adapter Content
The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.
Note that only samples with ≥ 0.1% adapter contamination are shown.
There may be several lines per sample, as one is shown for each adapter detected in the file.
From the FastQC Help:
The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.
Picard
Picard is a set of Java command line tools for manipulating high-throughput sequencing data.
Alignment Summary
Please note that Picard's read counts are divided by two for paired-end data. Total bases (including unaligned) is not provided.
Mean read length
The mean read length of the set of reads examined.
Base Distribution
Plot shows the distribution of bases by cycle.
GC Coverage Bias
This plot shows bias in coverage across regions of the genome with varying GC content. A perfect library would be a flat line at y = 1.
Insert Size
Plot shows the number of reads at a given insert size. Reads with different orientations are summed.
Mark Duplicates
Number of reads, categorised by duplication state. Pair counts are doubled - see help text for details.
The table in the Picard metrics file contains some columns referring read pairs and some referring to single reads.
To make the numbers in this plot sum correctly, values referring to pairs are doubled according to the scheme below:
READS_IN_DUPLICATE_PAIRS = 2 * READ_PAIR_DUPLICATESREADS_IN_UNIQUE_PAIRS = 2 * (READ_PAIRS_EXAMINED - READ_PAIR_DUPLICATES)READS_IN_UNIQUE_UNPAIRED = UNPAIRED_READS_EXAMINED - UNPAIRED_READ_DUPLICATESREADS_IN_DUPLICATE_PAIRS_OPTICAL = 2 * READ_PAIR_OPTICAL_DUPLICATESREADS_IN_DUPLICATE_PAIRS_NONOPTICAL = READS_IN_DUPLICATE_PAIRS - READS_IN_DUPLICATE_PAIRS_OPTICALREADS_IN_DUPLICATE_UNPAIRED = UNPAIRED_READ_DUPLICATESREADS_UNMAPPED = UNMAPPED_READS
Mean Base Quality by Cycle
Plot shows the mean base quality by cycle.
This metric gives an overall snapshot of sequencing machine performance. For most types of sequencing data, the output is expected to show a slight reduction in overall base quality scores towards the end of each read.
Spikes in quality within reads are not expected and may indicate that technical problems occurred during sequencing.
Base Quality Distribution
Plot shows the count of each base quality score.
WGS Coverage
The number of bases in the genome territory for each fold coverage. Note that final 1% of data is hidden to prevent very long tails.
WGS Filtered Bases
For more information about the filtered categories, see the Picard documentation.
FastQ Screen
0.15.3
FastQ Screen allows you to screen a library of sequences in FastQ format against a set of sequence databases so you can see if the composition of the library matches with what you expect.DOI: 10.12688/f1000research.15931.2.
Mapped Reads
FastQC
0.12.1
FastQC is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.
Sequence Counts
Sequence counts for each sample. Duplicate read counts are an estimate only.
This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).
You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:
Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.
The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.
Sequence Quality Histograms
The mean quality value across each base position in the read.
To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).
Taken from the FastQC help:
The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.
Per Sequence Quality Scores
The number of reads with average quality scores. Shows if a subset of reads has poor quality.
From the FastQC help:
The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.
Per Base Sequence Content
The proportion of each base position for which each of the four normal DNA bases has been called.
To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.
To see the data as a line plot, as in the original FastQC graph, click on a sample track.
From the FastQC help:
Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.
In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.
It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.
Rollover for sample name
Per Sequence GC Content
The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.
From the FastQC help:
This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.
In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.
An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.
Per Base N Content
The percentage of base calls at each position for which an N was called.
From the FastQC help:
If a sequencer is unable to make a base call with sufficient confidence then it will
normally substitute an N rather than a conventional base call. This graph shows the
percentage of base calls at each position for which an N was called.
It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.
Sequence Length Distribution
The distribution of fragment sizes (read lengths) found. See the FastQC help
Sequence Duplication Levels
The relative level of duplication found for every sequence.
From the FastQC Help:
In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (eg PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.
Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.
The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.
In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.
Overrepresented sequences by sample
The total amount of overrepresented sequences found in each library.
FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.
Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.
From the FastQC Help:
A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.
FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.
Top overrepresented sequences
Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.
| Overrepresented sequence |
|---|
Adapter Content
The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.
Note that only samples with ≥ 0.1% adapter contamination are shown.
There may be several lines per sample, as one is shown for each adapter detected in the file.
From the FastQC Help:
The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.
Status Checks
Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).
FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).
It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.
Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.
In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.
Software Versions
Software Versions lists versions of software tools extracted from file contents.
| Software | Version |
|---|---|
| FastQ Screen | 0.15.3 |
| FastQC | 0.12.1 |